



"""simple docstring"""





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase  			=		logging.get_logger(__name__)
def   a__      ( lowerCAmelCase__ ):
      UpperCAmelCase_     					=				SwinConfig(
          embed_dim=192      ,     depths=(2, 2, 18, 2)      ,     num_heads=(6, 12, 24, 48)      ,     window_size=12      ,     out_features=["stage2", "stage3", "stage4"]      ,     )

      UpperCAmelCase_     					=				DetaConfig(
          backbone_config=lowerCAmelCase__      ,     num_queries=900      ,     encoder_ffn_dim=2048      ,     decoder_ffn_dim=2048      ,     num_feature_levels=5      ,     assign_first_stage=lowerCAmelCase__      ,     with_box_refine=lowerCAmelCase__      ,     two_stage=lowerCAmelCase__      ,     )

      # set labels
      UpperCAmelCase_     					=				"huggingface/label-files"
      if "o365" in model_name:
            UpperCAmelCase_     					=				366
            UpperCAmelCase_     					=				"object365-id2label.json"
      else:
            UpperCAmelCase_     					=				91
            UpperCAmelCase_     					=				"coco-detection-id2label.json"

      UpperCAmelCase_     					=				num_labels
      UpperCAmelCase_     					=				json.load(open(cached_download(hf_hub_url(lowerCAmelCase__      ,     lowerCAmelCase__      ,     repo_type="dataset" ) )      ,     "r" ) )
      UpperCAmelCase_     					=				{int(lowerCAmelCase__ ): v for k, v in idalabel.items()}
      UpperCAmelCase_     					=				idalabel
      UpperCAmelCase_     					=				{v: k for k, v in idalabel.items()}

      return config
def   a__      ( lowerCAmelCase__ ):
      UpperCAmelCase_     					=				[]

      # stem
      # fmt: off
      rename_keys.append(("backbone.0.body.patch_embed.proj.weight", "model.backbone.model.embeddings.patch_embeddings.projection.weight") )
      rename_keys.append(("backbone.0.body.patch_embed.proj.bias", "model.backbone.model.embeddings.patch_embeddings.projection.bias") )
      rename_keys.append(("backbone.0.body.patch_embed.norm.weight", "model.backbone.model.embeddings.norm.weight") )
      rename_keys.append(("backbone.0.body.patch_embed.norm.bias", "model.backbone.model.embeddings.norm.bias") )
      # stages
      for i in range(len(config.backbone_config.depths ) ):
            for j in range(config.backbone_config.depths[i] ):
                  rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm1.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""") )
                  rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm1.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""") )
                  rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""") )
                  rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""") )
                  rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""") )
                  rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""") )
                  rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm2.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""") )
                  rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm2.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""") )
                  rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""") )
                  rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""") )
                  rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""") )
                  rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""") )

            if i < 3:
                  rename_keys.append((f"""backbone.0.body.layers.{i}.downsample.reduction.weight""", f"""model.backbone.model.encoder.layers.{i}.downsample.reduction.weight""") )
                  rename_keys.append((f"""backbone.0.body.layers.{i}.downsample.norm.weight""", f"""model.backbone.model.encoder.layers.{i}.downsample.norm.weight""") )
                  rename_keys.append((f"""backbone.0.body.layers.{i}.downsample.norm.bias""", f"""model.backbone.model.encoder.layers.{i}.downsample.norm.bias""") )

      rename_keys.append(("backbone.0.body.norm1.weight", "model.backbone.model.hidden_states_norms.stage2.weight") )
      rename_keys.append(("backbone.0.body.norm1.bias", "model.backbone.model.hidden_states_norms.stage2.bias") )
      rename_keys.append(("backbone.0.body.norm2.weight", "model.backbone.model.hidden_states_norms.stage3.weight") )
      rename_keys.append(("backbone.0.body.norm2.bias", "model.backbone.model.hidden_states_norms.stage3.bias") )
      rename_keys.append(("backbone.0.body.norm3.weight", "model.backbone.model.hidden_states_norms.stage4.weight") )
      rename_keys.append(("backbone.0.body.norm3.bias", "model.backbone.model.hidden_states_norms.stage4.bias") )

      # transformer encoder
      for i in range(config.encoder_layers ):
            rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight""", f"""model.encoder.layers.{i}.self_attn.sampling_offsets.weight""") )
            rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias""", f"""model.encoder.layers.{i}.self_attn.sampling_offsets.bias""") )
            rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.attention_weights.weight""", f"""model.encoder.layers.{i}.self_attn.attention_weights.weight""") )
            rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.attention_weights.bias""", f"""model.encoder.layers.{i}.self_attn.attention_weights.bias""") )
            rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.value_proj.weight""", f"""model.encoder.layers.{i}.self_attn.value_proj.weight""") )
            rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.value_proj.bias""", f"""model.encoder.layers.{i}.self_attn.value_proj.bias""") )
            rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.output_proj.weight""", f"""model.encoder.layers.{i}.self_attn.output_proj.weight""") )
            rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.output_proj.bias""", f"""model.encoder.layers.{i}.self_attn.output_proj.bias""") )
            rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.weight""", f"""model.encoder.layers.{i}.self_attn_layer_norm.weight""") )
            rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""model.encoder.layers.{i}.self_attn_layer_norm.bias""") )
            rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""model.encoder.layers.{i}.fc1.weight""") )
            rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""model.encoder.layers.{i}.fc1.bias""") )
            rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""model.encoder.layers.{i}.fc2.weight""") )
            rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""model.encoder.layers.{i}.fc2.bias""") )
            rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""model.encoder.layers.{i}.final_layer_norm.weight""") )
            rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""model.encoder.layers.{i}.final_layer_norm.bias""") )

      # transformer decoder
      for i in range(config.decoder_layers ):
            rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight""", f"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight""") )
            rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias""", f"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias""") )
            rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.attention_weights.weight""", f"""model.decoder.layers.{i}.encoder_attn.attention_weights.weight""") )
            rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.attention_weights.bias""", f"""model.decoder.layers.{i}.encoder_attn.attention_weights.bias""") )
            rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.value_proj.weight""", f"""model.decoder.layers.{i}.encoder_attn.value_proj.weight""") )
            rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.value_proj.bias""", f"""model.decoder.layers.{i}.encoder_attn.value_proj.bias""") )
            rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.output_proj.weight""", f"""model.decoder.layers.{i}.encoder_attn.output_proj.weight""") )
            rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.output_proj.bias""", f"""model.decoder.layers.{i}.encoder_attn.output_proj.bias""") )
            rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.weight""", f"""model.decoder.layers.{i}.encoder_attn_layer_norm.weight""") )
            rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.bias""", f"""model.decoder.layers.{i}.encoder_attn_layer_norm.bias""") )
            rename_keys.append((f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""model.decoder.layers.{i}.self_attn.out_proj.weight""") )
            rename_keys.append((f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""model.decoder.layers.{i}.self_attn.out_proj.bias""") )
            rename_keys.append((f"""transformer.decoder.layers.{i}.norm2.weight""", f"""model.decoder.layers.{i}.self_attn_layer_norm.weight""") )
            rename_keys.append((f"""transformer.decoder.layers.{i}.norm2.bias""", f"""model.decoder.layers.{i}.self_attn_layer_norm.bias""") )
            rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""model.decoder.layers.{i}.fc1.weight""") )
            rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""model.decoder.layers.{i}.fc1.bias""") )
            rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""model.decoder.layers.{i}.fc2.weight""") )
            rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""model.decoder.layers.{i}.fc2.bias""") )
            rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.weight""", f"""model.decoder.layers.{i}.final_layer_norm.weight""") )
            rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""model.decoder.layers.{i}.final_layer_norm.bias""") )

      # fmt: on

      return rename_keys
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ ):
      UpperCAmelCase_     					=				dct.pop(lowerCAmelCase__ )
      UpperCAmelCase_     					=				val
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__ ):
      UpperCAmelCase_     					=				[int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
      for i in range(len(backbone_config.depths ) ):
            UpperCAmelCase_     					=				num_features[i]
            for j in range(backbone_config.depths[i] ):
                  # fmt: off
                  # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
                  UpperCAmelCase_     					=				state_dict.pop(f"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight""" )
                  UpperCAmelCase_     					=				state_dict.pop(f"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias""" )
                  # next, add query, keys and values (in that order) to the state dict
                  UpperCAmelCase_     					=				in_proj_weight[:dim, :]
                  UpperCAmelCase_     					=				in_proj_bias[: dim]
                  UpperCAmelCase_     					=				in_proj_weight[
                      dim : dim * 2, :
                  ]
                  UpperCAmelCase_     					=				in_proj_bias[
                      dim : dim * 2
                  ]
                  UpperCAmelCase_     					=				in_proj_weight[
                      -dim :, :
                  ]
                  UpperCAmelCase_     					=				in_proj_bias[-dim :]
                  # fmt: on
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__ ):
      # transformer decoder self-attention layers
      UpperCAmelCase_     					=				config.d_model
      for i in range(config.decoder_layers ):
            # read in weights + bias of input projection layer of self-attention
            UpperCAmelCase_     					=				state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_weight""" )
            UpperCAmelCase_     					=				state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_bias""" )
            # next, add query, keys and values (in that order) to the state dict
            UpperCAmelCase_     					=				in_proj_weight[:hidden_size, :]
            UpperCAmelCase_     					=				in_proj_bias[:hidden_size]
            UpperCAmelCase_     					=				in_proj_weight[
                hidden_size : hidden_size * 2, :
            ]
            UpperCAmelCase_     					=				in_proj_bias[hidden_size : hidden_size * 2]
            UpperCAmelCase_     					=				in_proj_weight[-hidden_size:, :]
            UpperCAmelCase_     					=				in_proj_bias[-hidden_size:]
def   a__      ( ):
      UpperCAmelCase_     					=				"http://images.cocodataset.org/val2017/000000039769.jpg"
      UpperCAmelCase_     					=				Image.open(requests.get(lowerCAmelCase__      ,     stream=lowerCAmelCase__ ).raw )

      return im
@torch.no_grad()
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ ):
      UpperCAmelCase_     					=				get_deta_config(lowerCAmelCase__ )

      # load original state dict
      if model_name == "deta-swin-large":
            UpperCAmelCase_     					=				hf_hub_download(repo_id="nielsr/deta-checkpoints"      ,     filename="adet_swin_ft.pth" )
      elif model_name == "deta-swin-large-o365":
            UpperCAmelCase_     					=				hf_hub_download(repo_id="jozhang97/deta-swin-l-o365"      ,     filename="deta_swin_pt_o365.pth" )
      else:
            raise ValueError(f"""Model name {model_name} not supported""" )

      UpperCAmelCase_     					=				torch.load(lowerCAmelCase__      ,     map_location="cpu" )["model"]

      # original state dict
      for name, param in state_dict.items():
            print(lowerCAmelCase__      ,     param.shape )

      # rename keys
      UpperCAmelCase_     					=				create_rename_keys(lowerCAmelCase__ )
      for src, dest in rename_keys:
            rename_key(lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ )
      read_in_swin_q_k_v(lowerCAmelCase__      ,     config.backbone_config )
      read_in_decoder_q_k_v(lowerCAmelCase__      ,     lowerCAmelCase__ )

      # fix some prefixes
      for key in state_dict.copy().keys():
            if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
                  UpperCAmelCase_     					=				state_dict.pop(lowerCAmelCase__ )
                  UpperCAmelCase_     					=				val
            if "input_proj" in key:
                  UpperCAmelCase_     					=				state_dict.pop(lowerCAmelCase__ )
                  UpperCAmelCase_     					=				val
            if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
                  UpperCAmelCase_     					=				state_dict.pop(lowerCAmelCase__ )
                  UpperCAmelCase_     					=				val

    # finally, create HuggingFace model and load state dict
      UpperCAmelCase_     					=				DetaForObjectDetection(lowerCAmelCase__ )
      model.load_state_dict(lowerCAmelCase__ )
      model.eval()

      UpperCAmelCase_     					=				"cuda" if torch.cuda.is_available() else "cpu"
      model.to(lowerCAmelCase__ )

      # load image processor
      UpperCAmelCase_     					=				DetaImageProcessor(format="coco_detection" )

      # verify our conversion on image
      UpperCAmelCase_     					=				prepare_img()
      UpperCAmelCase_     					=				processor(images=lowerCAmelCase__      ,     return_tensors="pt" )
      UpperCAmelCase_     					=				encoding["pixel_values"]
      UpperCAmelCase_     					=				model(pixel_values.to(lowerCAmelCase__ ) )

      # verify logits
      print("Logits:"      ,     outputs.logits[0, :3, :3] )
      print("Boxes:"      ,     outputs.pred_boxes[0, :3, :3] )
      if model_name == "deta-swin-large":
            UpperCAmelCase_     					=				torch.tensor(
                [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]] )
            UpperCAmelCase_     					=				torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]] )
      elif model_name == "deta-swin-large-o365":
            UpperCAmelCase_     					=				torch.tensor(
                [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]] )
            UpperCAmelCase_     					=				torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]] )
      assert torch.allclose(outputs.logits[0, :3, :3]      ,     expected_logits.to(lowerCAmelCase__ )      ,     atol=1e-4 )
      assert torch.allclose(outputs.pred_boxes[0, :3, :3]      ,     expected_boxes.to(lowerCAmelCase__ )      ,     atol=1e-4 )
      print("Everything ok!" )

      if pytorch_dump_folder_path:
            # Save model and processor
            logger.info(f"""Saving PyTorch model and processor to {pytorch_dump_folder_path}...""" )
            Path(lowerCAmelCase__ ).mkdir(exist_ok=lowerCAmelCase__ )
            model.save_pretrained(lowerCAmelCase__ )
            processor.save_pretrained(lowerCAmelCase__ )

      # Push to hub
      if push_to_hub:
            print("Pushing model and processor to hub..." )
            model.push_to_hub(f"""jozhang97/{model_name}""" )
            processor.push_to_hub(f"""jozhang97/{model_name}""" )


if __name__ == "__main__":
  lowerCamelCase  			=		argparse.ArgumentParser()

  parser.add_argument(
      """--model_name""",
      type=str,
      default="""deta-swin-large""",
      choices=["""deta-swin-large""", """deta-swin-large-o365"""],
      help="""Name of the model you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""",
      default=None,
      type=str,
      help="""Path to the folder to output PyTorch model.""",
  )
  parser.add_argument(
      """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
  )
  lowerCamelCase  			=		parser.parse_args()
  convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)












'''simple docstring'''







import argparse
import os
import re


__lowerCAmelCase        =							"src/diffusers"

# Pattern that looks at the indentation in a line.
__lowerCAmelCase        =							re.compile(R"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
__lowerCAmelCase        =							re.compile(R"^\s*\"([^\"]+)\":")
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
__lowerCAmelCase        =							re.compile(R"^\s*_import_structure\[\"([^\"]+)\"\]")
# Pattern that matches `"key",` and puts `key` in group 0.
__lowerCAmelCase        =							re.compile(R"^\s*\"([^\"]+)\",\s*$")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
__lowerCAmelCase        =							re.compile(R"\[([^\]]+)\]")


def       __UpperCamelCase					(      lowercase_       :  Any   ):
       """simple docstring"""




       a_						     =			_re_indent.search(lowercase_   )
       return "" if search is None else search.groups()[0]


def       __UpperCamelCase					(      lowercase_       :  Union[str, Any]   ,       lowercase_       :  int=""   ,       lowercase_       :  Union[str, Any]=None   ,       lowercase_       :  str=None   ):
       """simple docstring"""




       a_						     =			0
       a_						     =			code.split('\n'   )
       if start_prompt is not None:
              while not lines[index].startswith(lowercase_   ):
                     index += 1
              a_						     =			['\n'.join(lines[:index]   )]
       else:
              a_						     =			[]

       # We split into blocks until we get to the `end_prompt` (or the end of the block).
       a_						     =			[lines[index]]
       index += 1
       while index < len(lowercase_   ) and (end_prompt is None or not lines[index].startswith(lowercase_   )):
              if len(lines[index]   ) > 0 and get_indent(lines[index]   ) == indent_level:
                     if len(lowercase_   ) > 0 and get_indent(current_block[-1]   ).startswith(indent_level + ' '   ):
                            current_block.append(lines[index]   )
                            blocks.append('\n'.join(lowercase_   )   )
                            if index < len(lowercase_   ) - 1:
                                   a_						     =			[lines[index + 1]]
                                   index += 1
                            else:
                                   a_						     =			[]
                     else:
                            blocks.append('\n'.join(lowercase_   )   )
                            a_						     =			[lines[index]]
              else:
                     current_block.append(lines[index]   )
              index += 1

       # Adds current block if it's nonempty.
       if len(lowercase_   ) > 0:
              blocks.append('\n'.join(lowercase_   )   )

       # Add final block after end_prompt if provided.
       if end_prompt is not None and index < len(lowercase_   ):
              blocks.append('\n'.join(lines[index:]   )   )

       return blocks


def       __UpperCamelCase					(      lowercase_       :  Optional[int]   ):
       """simple docstring"""




       def _inner(lowercase_       :  Union[str, Any]   ):
              return key(lowercase_   ).lower().replace('_'   ,       ''   )

       return _inner


def       __UpperCamelCase					(      lowercase_       :  List[Any]   ,       lowercase_       :  int=None   ):
       """simple docstring"""




       def noop(lowercase_       :  str   ):
              return x

       if key is None:
              a_						     =			noop
       # Constants are all uppercase, they go first.
       a_						     =			[obj for obj in objects if key(lowercase_   ).isupper()]
       # Classes are not all uppercase but start with a capital, they go second.
       a_						     =			[obj for obj in objects if key(lowercase_   )[0].isupper() and not key(lowercase_   ).isupper()]
       # Functions begin with a lowercase, they go last.
       a_						     =			[obj for obj in objects if not key(lowercase_   )[0].isupper()]

       a_						     =			ignore_underscore(lowercase_   )
       return sorted(lowercase_   ,       key=lowercase_   ) + sorted(lowercase_   ,       key=lowercase_   ) + sorted(lowercase_   ,       key=lowercase_   )


def       __UpperCamelCase					(      lowercase_       :  Optional[Any]   ):
       """simple docstring"""




       def _replace(lowercase_       :  List[Any]   ):
              a_						     =			match.groups()[0]
              if "," not in imports:
                     return F'[{imports}]'
              a_						     =			[part.strip().replace('"'   ,       ''   ) for part in imports.split(','   )]
              # We will have a final empty element if the line finished with a comma.
              if len(keys[-1]   ) == 0:
                     a_						     =			keys[:-1]
              return "[" + ", ".join([F'"{k}"' for k in sort_objects(lowercase_   )]   ) + "]"

       a_						     =			import_statement.split('\n'   )
       if len(lowercase_   ) > 3:
              # Here we have to sort internal imports that are on several lines (one per name):
              # key: [
              #     "object1",
              #     "object2",
              #     ...
              # ]

              # We may have to ignore one or two lines on each side.
              a_						     =			2 if lines[1].strip() == '[' else 1
              a_						     =			[(i, _re_strip_line.search(lowercase_   ).groups()[0]) for i, line in enumerate(lines[idx:-idx]   )]
              a_						     =			sort_objects(lowercase_   ,       key=lambda lowercase_   : x[1]   )
              a_						     =			[lines[x[0] + idx] for x in sorted_indices]
              return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]   )
       elif len(lowercase_   ) == 3:
              # Here we have to sort internal imports that are on one separate line:
              # key: [
              #     "object1", "object2", ...
              # ]
              if _re_bracket_content.search(lines[1]   ) is not None:
                     a_						     =			_re_bracket_content.sub(_replace   ,       lines[1]   )
              else:
                     a_						     =			[part.strip().replace('"'   ,       ''   ) for part in lines[1].split(','   )]
                     # We will have a final empty element if the line finished with a comma.
                     if len(keys[-1]   ) == 0:
                            a_						     =			keys[:-1]
                     a_						     =			get_indent(lines[1]   ) + ', '.join([F'"{k}"' for k in sort_objects(lowercase_   )]   )
              return "\n".join(lowercase_   )
       else:
              # Finally we have to deal with imports fitting on one line
              a_						     =			_re_bracket_content.sub(_replace   ,       lowercase_   )
              return import_statement


def       __UpperCamelCase					(      lowercase_       :  Dict   ,       lowercase_       :  List[Any]=True   ):
       """simple docstring"""




       with open(lowercase_   ,       'r'   ) as f:
              a_						     =			f.read()

       if "_import_structure" not in code:
              return

       # Blocks of indent level 0
       a_						     =			split_code_in_indented_blocks(
           lowercase_   ,       start_prompt='_import_structure = {'   ,       end_prompt='if TYPE_CHECKING:'   )

       # We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
       for block_idx in range(1   ,       len(lowercase_   ) - 1   ):
              # Check if the block contains some `_import_structure`s thingy to sort.
              a_						     =			main_blocks[block_idx]
              a_						     =			block.split('\n'   )

              # Get to the start of the imports.
              a_						     =			0
              while line_idx < len(lowercase_   ) and "_import_structure" not in block_lines[line_idx]:
                     # Skip dummy import blocks
                     if "import dummy" in block_lines[line_idx]:
                            a_						     =			len(lowercase_   )
                     else:
                            line_idx += 1
              if line_idx >= len(lowercase_   ):
                     continue

              # Ignore beginning and last line: they don't contain anything.
              a_						     =			'\n'.join(block_lines[line_idx:-1]   )
              a_						     =			get_indent(block_lines[1]   )
              # Slit the internal block into blocks of indent level 1.
              a_						     =			split_code_in_indented_blocks(lowercase_   ,       indent_level=lowercase_   )
              # We have two categories of import key: list or _import_structure[key].append/extend
              a_						     =			_re_direct_key if '_import_structure' in block_lines[0] else _re_indirect_key
              # Grab the keys, but there is a trap: some lines are empty or just comments.
              a_						     =			[(pattern.search(lowercase_   ).groups()[0] if pattern.search(lowercase_   ) is not None else None) for b in internal_blocks]
              # We only sort the lines with a key.
              a_						     =			[(i, key) for i, key in enumerate(lowercase_   ) if key is not None]
              a_						     =			[x[0] for x in sorted(lowercase_   ,       key=lambda lowercase_   : x[1]   )]

              # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
              a_						     =			0
              a_						     =			[]
              for i in range(len(lowercase_   )   ):
                     if keys[i] is None:
                            reordered_blocks.append(internal_blocks[i]   )
                     else:
                            a_						     =			sort_objects_in_import(internal_blocks[sorted_indices[count]]   )
                            reordered_blocks.append(lowercase_   )
                            count += 1

        # And we put our main block back together with its first and last line.
              a_						     =			'\n'.join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]]   )

       if code != "\n".join(lowercase_   ):
              if check_only:
                     return True
              else:
                     print(F'Overwriting {file}.'   )
                     with open(lowercase_   ,       'w'   ) as f:
                            f.write('\n'.join(lowercase_   )   )


def       __UpperCamelCase					(      lowercase_       :  int=True   ):
       """simple docstring"""




       a_						     =			[]
       for root, _, files in os.walk(lowercase_   ):
              if "__init__.py" in files:
                     a_						     =			sort_imports(os.path.join(lowercase_   ,       '__init__.py'   )   ,       check_only=lowercase_   )
                     if result:
                            a_						     =			[os.path.join(lowercase_   ,       '__init__.py'   )]
       if len(lowercase_   ) > 0:
              raise ValueError(F'Would overwrite {len(lowercase_   )} files, run `make style`.'   )


if __name__ == "__main__":
  __lowerCAmelCase        =							argparse.ArgumentParser()
  parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
  __lowerCAmelCase        =							parser.parse_args()

  sort_imports_in_all_inits(check_only=args.check_only)




